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Detection of land cover changes in El Rawashda forest, Sudan: A systematic comparison

Band 11 der Reihe Fernerkundung und angewandte Geoinformatik

39,80 € Preisreferenz Lieferbar in 2-3 Tagen


Wafa Mohamed Tahir Nori
Detection of land cover changes in El Rawashda forest, Sudan: A systematic comparison
Band 11 der Reihe „Fernerkundung und angewandte Geoinformatik“
Herausgegeben von Univ. Prof. Dr. habil. Elmar Csaplovics, Lehrstuhl Remote Sensing, FR Geowissenschaften, TU Dresden
134 Seiten, Format DIN B5, Zahlreiche Abbildungen, davon 34 farbig. Sprache: Englisch. Preis: 39,80 Euro. ISBN 978-3-944101-20-0. Rhombos-Verlag, Berlin 2014
About this book
This research evaluates the potential of remote sensing for monitoring forest cover change in El Rawashda forest, Sudan, using Landsat ETM and Terra ASTER imagery. This was accomplished by performing eight change detection algorithms. Firstly a simplified post-classification with only 4 forest classes, namely close forest, open forest, bare land and grass land, was used. A RGB-NDVI change detection strategy to detect major decrease or increase in forest vegetation was developed as well. This method was found to be more effective than NDVI image differencing as it distinguishes different change classes by different colour tones. The Tasseled Cap green layer (GTC) composite was proposed to detect the change in vegetation of the study area. This method performs better than RGB-NDVI. Change vector analysis (CVA) based on Tasseled Cap transformation (TCT) was also applied for detecting and characterizing land cover change. The calculated date to date change vectors contain useful information, both in their magnitude and their direction. A powerful tool for time series analysis is the Principal Components Analysis (PCA). This method was tested for change detection in the study area by two ways: Multitemporal PCA and Selective PCA. A recently proposed approach, the Multivariate Alteration Detection (MAD), in combination with a posterior Maximum Autocorrelation Factor Transformation (MAF) was used to demonstrate visualization of vegetation changes in the study area. As a final step a quantitative accuracy assessment at the level of change/no change pixels was performed. Among the various investigated methods of forest cover change analysis the highest accuracy was obtained using post-classification comparison based on supervised classification.


Titel: Detection of land cover changes in El Rawashda forest, Sudan: A systematic comparison
Autoren/Herausgeber: Wafa Mohamed Tahir Nori, Elmar Csaplovics (Hrsg.)
Aus der Reihe: Fernerkundung und angewandte Geoinformatik
Ausgabe: 1. Auflage

ISBN/EAN: 9783944101200

Seitenzahl: 134
Format: 25 x 17,6 cm
Produktform: Taschenbuch/Softcover
Gewicht: 300 g
Sprache: Englisch

The author
Wafa Mohamed Tahir Nori holds a bachelor degree, in forest studies in 1994 from University of Khartoum, Faculty of Agriculture, a master degree in forest pathology in 1996 from University of Khartoum, Faculty of Forestry, and a doctoral degree in remote sensing in 2012 from Technische Universität Dresden, Germany. The author started her work experience during her master research as part time teaching assistant at University of Khartoum and University of Juba from 1994 to 1996, and then was affiliated to University of Kordofan, Faculty of Natural Resources and Environmental Studies, El Obeid, Sudan. Currently she has been promoted to assistant professor and continues research in applications of remote sensing in the monitoring of drylands.
The editor/Der Herausgeber
Prof. Dr. techn. habil. Elmar Csaplovics leitet den Lehrstuhl Geofernerkundung am Institut für Photogrammetrie und Fernerkundung der Technischen Universiaet Dresden

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